'''
import gymnasium as gym
env = gym.make("CartPole-v1")
observation, info = env.reset()

for _ in range(1):
    action = env.action_space.sample()  # agent policy that uses the observation and info
    observation, reward, terminated, truncated, info = env.step(action)

    print(type(observation))

    if terminated or truncated:
        observation, info = env.reset()

env.close()
'''

a=10
print(a//2,a/2)